An Empirical Examination of Balancing Strategy for Counterfactual Estimation on Time Series
This work addresses a gap in applying balancing strategies to time series data for researchers and practitioners in fields like healthcare and finance, but it is incremental as it focuses on empirical validation rather than introducing new methods.
The paper examines the effectiveness of balancing strategies for counterfactual estimation on time series data, finding that their robustness and applicability in temporal settings require reexamination based on empirical tests across multiple datasets.
Counterfactual estimation from observations represents a critical endeavor in numerous application fields, such as healthcare and finance, with the primary challenge being the mitigation of treatment bias. The balancing strategy aimed at reducing covariate disparities between different treatment groups serves as a universal solution. However, when it comes to the time series data, the effectiveness of balancing strategies remains an open question, with a thorough analysis of the robustness and applicability of balancing strategies still lacking. This paper revisits counterfactual estimation in the temporal setting and provides a brief overview of recent advancements in balancing strategies. More importantly, we conduct a critical empirical examination for the effectiveness of the balancing strategies within the realm of temporal counterfactual estimation in various settings on multiple datasets. Our findings could be of significant interest to researchers and practitioners and call for a reexamination of the balancing strategy in time series settings.